
Need for a good control
A good control group is crucial.
To assess the effect of an intervention we need to compare a test and control group
This is often not possible in a pretest/posttest design: e.g. effect before and after administring a drug without the use of a placebo group
Groups in an observational study are often not comparable: advanced statistical methods are required to draw causal conclusions.
Double blinding
We have to be aware of confounding!
Randomized studies: random assignment of subjects in the study to the different treatment arms \(\rightarrow\) comparable groups.
Randomisation
- Randomisation completely at random (no systematic allocation)
Balanced randomisation
- Equal numbers of each treatment are assigned to a block of 2 or 4 patients.
- AB, (2) BA
- AABB, (2) ABAB, (3) ABBA, (4) BABA, (5) BAAB, (6) BBAA
Gebalanced randomisation ensures \(\pm\) the same number of people in the control and the treatment arm of the experiment.
Does not make that we have an equal number of males with and without the treatment, etc.
In small studies it is possible that the groups are inbalanced in other characteristics (e.g. gender, race, age …)
This is not problematic because it occurs at random, but, again it causes a loss in precision.
Stratified randomization**
- The imbalance according to for instance gender can be avoided using stratified randomisation: balanced randomisatie per stratum
Wrap-up
Sample size is very important
To assess the effect of a treatment we should compare comparable and representative groups of subjects with and without the treatment (a good control!).
In observational studies the researcher cannot choose the treatment. It was the patient or their MD who had chosen it
In experimental studies the researcher assigns the treatment
Confounding can be avoided via randomisation
We can also correct for confounding in the statistical analysis for the confounders that have been registered.
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